Prevention of major depressive disorder (MDD) is a public health priority, and is in critical need of innovative strategies that preemptively identify those at-risk in order to enable early intervention. A recent meta-analysis of over 20 longitudinal studies found the risk for incident depression among individuals with insomnia disorder is nearly three times that for normal sleepers, thus making insomnia a potential point of entry for depression prevention. Identification and treatment of insomnia typically occurs in primary care, and is commonly treated with hypnotic medications; however, hypnotics have significant limitations, including increased risk for residual impairment, falls in the elderly, and abuse. Cognitive behavioral treatment of insomnia (CBT-I) has been recommended as a first line approach with demonstrated efficacy that is sustained beyond initial therapeutic intervention. However, effective and widespread implementation of CBT-I is severely limited by the national shortage of trained practitioners in clinical practice. A stepped care approach rooted in primary care holds potential for innovative accessibility and delivery of CBT-I, improving insomnia therapeutics, and reducing rates of MDD by targeting a robust yet modifiable risk factor in insomnia. Our proposed stepped care model uses digital cognitive behavioral therapy (dCBT-I) as an accessible, least-restrictive, first line intervention that reduces specialist time and resources, and adds clinician based face-to-face CBT-I only for refractory patients who need a more personalized, flexible, and durable therapist driven approach. We propose a large-scale clinical trial in the primary care setting that utilizes a stepped care model (SMART design) to determine the effectiveness of dCBT-I alone and in combination with face-to-face CBT-I for insomnia, and the effects of these sleep interventions on the prevention of MDD. An important innovative component of the trial is the 1 and 2-year follow-up assessments to determine the durability of effectiveness over time and assess the impact on depression incidence and relapse. Early risk-detection and prevention is especially critical in those at elevated risk for depression to reduce health disparities. Thus, individuals with significant vulnerability to MDD, such as high sleep-reactivity, low socioeconomic status, and racial minorities will be included in significant numbers to test for potential moderation of treatment effects stratified by risk. Finally, improving sleep through insomnia treatment may reduce nocturnal rumination, which may mitigate progression toward MDD. As such, we will determine whether changes in nocturnal rumination (i.e., target), a modifiable risk-factor, mediates the effects of CBT-I and dCBT-I on MDD incidence and relapse. This project will test a highly scalable model of sleep care in a large primary care system to determine the potential for wide dissemination to address the high volume of population need for safe and effective insomnia treatment and associated prevention of depression.